ArticleJMIR medical education2024
Comprehensiveness, Accuracy, and Readability of Exercise Recommendations Provided by an AI-Based Chatbot: Mixed Methods Study.
Article in JMIR medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07238556 (Feasibility and Acceptability of a Novel Algorithm for Physicians to Prescribe Personalized Exercise Prescriptions to Patients With Cardiovascular Disease Risk Factors), which is not on this map. Cited by 29 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Feasibility and Acceptability of a Novel Algorithm for Physicians to Prescribe Personalized Exercise Prescriptions to Patients With Cardiovascular Disease Risk Factors: Study Protocol for an Exploratory Randomized Controlled Crossover Trial
Who cites it
29 citing papers in PubMed.
- Article
- Toward Precision Cardiac Rehabilitation: Current Limitations and Future Opportunities of Omics and Artificial Intelligence.Sports medicine (Auckland, N.Z.) · 2026Review
- Prescribed Rest Intervals in Home-Based Exercise Therapy and Rehabilitation: A Narrative Review and Conceptual Framework for Temporal Fidelity.Healthcare (Basel, Switzerland) · 2026Review
- User Acceptability and Adoption of AI-Generated Lifestyle Intervention Recommendations: Scoping Review and Theoretical Integration.Journal of medical Internet research · 2026Article
- AI-Generated Exercise Prescriptions for At-Risk Populations: Safety and Feasibility of a Large Language Model Assessed by Expert Evaluation.Journal of clinical medicine · 2026Article
- Large Language Model-Based Agents for Physical Activity and Cognitive Training: Scoping Review.JMIR AI · 2026Review
- The Role of Artificial Intelligence and Professional Expertise in Adapted Physical Activity Prescription for Orthopedic Rehabilitation.Journal of functional morphology and kinesiology · 2026Article
- ChatGPT Outperforms Personal Trainers in Answering Common Exercise Training Questions.Journal of sports science & medicine · 2026Article
- Clinical Validation of an On-Device AI-Driven Real-Time Human Pose Estimation and Exercise Prescription Program; Prospective Single-Arm Quasi-Experimental Study.Healthcare (Basel, Switzerland) · 2026Article
- Feasibility and Acceptability of a Novel Algorithm for Physicians to Prescribe Personalized Exercise Prescriptions to Patients with Cardiovascular Disease Risk Factors: Study Protocol for an Exploratory Randomized Controlled Crossover Trial.Healthcare (Basel, Switzerland) · 2026Article
- Comparative analysis of text readability and writing styles in AI-generated vs. Human-written academic abstracts.PloS one · 2026Article
- The AI recommendation paradox: a systematic review evaluating the promise, peril, and path forward for large language models in exercise recommendation.Biology of sport · 2026Review
- The role of artificial intelligence in sports training: opportunities, challenges and future applications for competitive swimming.Biology of sport · 2026Article
- Evaluating the appropriateness and safety of generative AI in delivering lifestyle guidance for atrial fibrillation patients.Scientific reports · 2025Article
- Comparative evaluation of ChatGPT versions in training program design: scientific approach, accuracy, and practical applicability.BMC sports science, medicine & rehabilitation · 2025Article
- Evaluation Strategies for Large Language Model-Based Models in Exercise and Health Coaching: Scoping Review.Journal of medical Internet research · 2025Article
- Observational
- Article
- Accuracy of chat-based artificial intelligence for patient education on orthostatic hypotension.Clinical autonomic research : official journal of the Clinical Autonomic Research Society · 2025Article
- Assessment of Recommendations Provided to Athletes Regarding Sleep Education by GPT-4o and Google Gemini: Comparative Evaluation Study.JMIR formative research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRegular physical activity is critical for health and disease prevention. Yet, health care providers and patients face barriers to implement evidence-based lifestyle recommendations. The potential to augment care with the increased availability of artificial intelligence (AI) technologies is limitless; however, the suitability of AI-generated exercise recommendations has yet to be explored.
objectiveThe purpose of this study was to assess the comprehensiveness, accuracy, and readability of individualized exercise recommendations generated by a novel AI chatbot.
methodsA coding scheme was developed to score AI-generated exercise recommendations across ten categories informed by gold-standard exercise recommendations, including (1) health condition-specific benefits of exercise, (2) exercise preparticipation health screening, (3) frequency, (4) intensity, (5) time, (6) type, (7) volume, (8) progression, (9) special considerations, and (10) references to the primary literature. The AI chatbot was prompted to provide individualized exercise recommendations for 26 clinical populations using an open-source application programming interface. Two independent reviewers coded AI-generated content for each category and calculated comprehensiveness (%) and factual accuracy (%) on a scale of 0%-100%. Readability was assessed using the Flesch-Kincaid formula. Qualitative analysis identified and categorized themes from AI-generated output.
resultsAI-generated exercise recommendations were 41.2% (107/260) comprehensive and 90.7% (146/161) accurate, with the majority (8/15, 53%) of inaccuracy related to the need for exercise preparticipation medical clearance. Average readability level of AI-generated exercise recommendations was at the college level (mean 13.7, SD 1.7), with an average Flesch reading ease score of 31.1 (SD 7.7). Several recurring themes and observations of AI-generated output included concern for liability and safety, preference for aerobic exercise, and potential bias and direct discrimination against certain age-based populations and individuals with disabilities.
conclusionsThere were notable gaps in the comprehensiveness, accuracy, and readability of AI-generated exercise recommendations. Exercise and health care professionals should be aware of these limitations when using and endorsing AI-based technologies as a tool to support lifestyle change involving exercise.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.